An Improved Simplified Method of Power Amplifier Behavioral Model Based on Compressed Sensing Theory
This paper proposed an improved algorithm,which combines the advantages of Regularized Sparse Adaptive Matching Pursuit(RSAMP)algorithm and Orthogonal Matching Pursuit(OMP)algorithm to prune the redundant terms of power amplifier(PA)behavioral models.By processing the two atomic index sets of this two algorithms,the intersection and complement set are obtained,and the correct atom index in the intersection is contained with great probability.We optimize the complement set by select the most correct atomic index from the complement set,and then combine the most correct atomic index with the intersection to get the final index set.This method can reduce the probability of missing the correct atomic index greatly to get a more accurate sparse model.Simulation results show that the proposed fusion algorithm can efficiently construct a sparse behavioral model with very few terms,and the running time can be reduced to a suitable level.
Terms-Power amplifier (PA) Behavioral model (BM) Compressed sensing regularized sparse adaptive matching pursuit (RSAMP) orthogonal matching pursuit (OMP)
Yi Jin Mingyu Li Changzhi Xu Guangwen Yang Dizhu WANG Li Yang Jinzhong Zuo Xuejiao Zhang
China Academy of Space Technology of Xi”an,Xi”an 710100,China College of communication engineering,Chongqing University,Chongqing 400044,China
国内会议
武汉
英文
1-5
2017-09-17（万方平台首次上网日期，不代表论文的发表时间）